Fairing skin repair method based on measured wing data

ABSTRACT

A fairing skin repair method based on measured wing data includes fairing skin registration. Data set P1 through denoising and filtering wing point cloud data is reorganized to obtain a key point set P. A histogram feature descriptor in a normal direction of any key point in set P and a skin point cloud data Q is calculated. Euclidean distance between feature descriptors of two points is calculated through K-nearest neighbor algorithm, and points with high similarity are added into a set M. A clustering is performed on set M using a Hough voting algorithm to obtain a local point cloud set P′ in set P. The method includes fairing skin repair. The boundary line of the point frame is projected onto Q, and a distance between a projection line on the point cloud and the boundary line is calculated to obtain an amount of skin to be repaired.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority from Chinese PatentApplication No. 202010385403.8, filed on May 9, 2020. The content of theaforementioned applications, including any intervening amendmentthereto, is incorporated herein by reference in its entirety.

TECHNICAL FIELD

This application relates to three-dimensional model processing, and moreparticularly to fairing skin repair method based on measured wing data.

BACKGROUND

An aircraft is generally manufactured by assembling parts intosubcomponents and then components to form a fuselage and wings, andfinally assembling the fuselage and the wings together. Assembling wingsand fuselage is an important action for the aircraft assembly afterwhich the fairing skin repair is very important.

Aircraft skin parts are widely used in the wings and fuselage,accounting for about 30% of the entire sheet metal part, and theyusually have complex and diverse shapes and large dimensions. The skinis an important component to constitute the aerodynamic shape of theaircraft. The skin manufacturing not only requires the shape accuracyand mechanical performance, but also has strict requirements on thesurface quality. Currently, during a skin repair, the repairingallowance is gradually adjusted through manual comparison, marking andfinal comparison. The adjustment is labor intensive and has a lowefficiency. In addition, the accuracy of skin repair is difficult toguarantee.

SUMMARY

Aiming at the defects in the prior art, the present disclosure providesa fairing skin repair method based on measured wing data.

The technical solutions of the present disclosure are described asfollows.

A fairing skin repair method based on measured wing data, comprising:

S1) carrying out a fairing skin registration, comprising:

-   -   S101) obtaining skin point cloud data Q;    -   S102) performing denoising and voxel grid filtering on a known        point cloud data of docked wing to obtain a data set P1;        reorganizing the data set P1 through a data reorganization        method to obtain a key point set P; and calculating a normal        line of each key point in the key point set P;    -   S103) calculating a histogram feature descriptor in a normal        direction of any key point in the key point set P and a        histogram feature descriptor in a normal direction of any point        in the skin point cloud data Q, respectively;    -   S104) calculating a Euclidean distance between feature        descriptors of two points through a K-nearest neighbor        algorithm; searching similar histogram feature descriptors;        adding points with high similarity to a set M; and initially        setting the set M as an empty set;    -   S105) performing a clustering on the set M using a Hough voting        algorithm to obtain a local point cloud set P′ in the key point        set P that matches the skin point cloud data Q; and    -   S106) matching the skin point cloud data Q with the local point        cloud set P′ through an iterative closest point algorithm; and

S2) carrying out a fairing skin repair.

In some embodiments, in step (S101), a skin uniformly manufactured in afactory is scanned using a three-dimensional laser scanner, so as tocollect the skin point cloud data Q.

In some embodiments, the step (S102) comprises:

S102-1) preprocessing the known point cloud data of the docked wing toeliminate noise points that deviate from a contour;

S102-2) filtering the preprocessed point cloud data of the docked wingthrough voxel grid filtering to obtain the data set P1;

S102-3) taking a nearest neighbor of any key point in the data set P1,performing a search through a k-nearest neighbor algorithm—the datareorganization method to reorganize the data set P1 according to a treestructure, so as to obtain the key point set P; and

S102-4) reducing dimensionality of each adjacent point of the key pointset P from a three-dimensional plane to a two-dimensional plane throughprincipal component analysis; wherein the two-dimensional plane is atangent plane of the adjacent point, and a normal line of the tangentplane is the normal line of the corresponding key point.

In some embodiments, the step (S103) comprises:

S103-1) calculating a local feature descriptor m_(i), i=1, 2, 3, . . . ,k in a normal direction of any key point in the key point set P, whereink is the number of key points in the key point set;

wherein the step (S103-1) comprises:

taking any key point in the key point set P as a center; constructing aspherical area with a self-set radius; dividing grids along threedirections of radial, azimuth, and elevation; wherein the spherical areais divided into 32 spatial areas through dividing along the radialdirection 2 times, the azimuth direction 8 times, and the elevationdirection 2 times;

in each spatial region, calculating a cosine of an angle between anormal line n_(N) of any point in the spatial region and a normal linen_(i) of a key point p_(i): cos θ=n_(N)·n_(i); wherein N is the numberof points in the spatial region; and

performing a histogram statistic on the number of points falling intoeach spatial region according to the cosine value to obtain the localfeature descriptor m_(i) of the normal direction of the key point;

S103-2) calculating a local feature descriptor m_(j), j=1, 2, 3, . . . ,l in a normal direction of any point in the skin point cloud data Qusing a same method, wherein l is the number of key points in the skinpoint cloud data Q.

In some embodiments, the step (S104) comprises:

S104-1) inputting the local feature descriptor m_(i) of the histogram ofthe key point set P using KdTree; and performing a nearest neighborsearching using fast library for approximate nearest neighbors (FLANK);

S104-2) among all the points in the skin point cloud data Q, searching apoint whose matching distance from any key point in the key point set Pis less than a Euclidean distance σ, that is, a feature point: σ=0.3;and

S104-3) putting all the feature points whose matching distance is lessthan σ into the set M.

In some embodiments, the step (S105) comprises:

S105-1) calculating a local reference frame for the feature points inthe skin point cloud data Q and the feature points in the key point setP;

S105-2) performing a clustering using the Hough voting algorithm; forthe input feature points of the skin point cloud data Q and the inputfeature points of the key point set P, setting a size of a Hough peakpoint in a Hough space as a threshold; and

S105-3) matching the set M, according to the threshold set in step(S105-2), identifying a final cluster set, that is, the local pointcloud set P′.

In some embodiments, the step (S106) comprises:

S106-1) matching the skin point cloud data Q with the locked local pointcloud set P′ using the iterative closest point algorithm;

wherein the step (S106) comprises:

calculating a corresponding near point, that is, a corresponding pointpair of any key point in the key point set P in the skin point clouddata Q; obtaining a rigid body transformation T that minimizes anaverage distance of the corresponding point pair; obtaining atranslation parameter ω and a rotation parameter r; transforming the keypoint set P according to the translation parameter ω and a rotationparameter r to obtain a new transformed point set P″; wherein if the newtransformed point set P″ and the skin point cloud data Q satisfy that anaverage distance between the two point sets is less than a giventhreshold, a result after coarse registration will be obtained; and

S106-2) filtering out wrong points in the coarse registration using aglobal hypothesis verification algorithm, so as to finish a skinregistration.

In some embodiments, the step (S2) comprises:

S201) extracting a boundary line of a point cloud frame of the wing andthe skin point cloud data Q after coarse registration using a randomsample consensus (RANSAC) extraction algorithm;

S202) projecting the boundary line of the point frame onto the skinpoint cloud data Q; calculating a distance between a projection line onthe point cloud and the boundary line, so as to obtain an amount of skinto be repaired; and

S203) cutting the skin according to the amount of skin to be repaired;and finishing the repair.

The beneficial effects of the present disclosure are described asfollows.

In the present disclosure, fairing skin registration and fairing skinrepair are completed through collecting the skin point cloud data andthe wing point cloud data by means of a computer program. In this way,manpower is greatly saved, and the production efficiency is improved. Inaddition, without the influence of subjective factors, the registrationresult is more accurate. The staff can repair the skin according to anamount of skin to be repaired and effectively finish the repair.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart of a fairing skin repair method based on measuredwing data according to an embodiment of the present disclosure;

FIG. 2 is a flowchart of a method of a fairing skin registrationaccording to an embodiment of the present disclosure; and

FIG. 3 is a flowchart of a method of a fairing skin repair according toan embodiment of the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS

A fairing skin repair method based on measured wing data of the presentdisclosure will be further described clearly with reference to theaccompanying drawings and embodiments.

As shown in FIGS. 1-2, the fairing skin repair method based on measuredwing data includes the following steps.

S1) Fairing skin registration

-   -   S101) Skin point cloud data Q is obtained, and specifically, a        skin uniformly manufactured in a factory is scanned using a        three-dimensional laser scanned, so as to collect the skin point        cloud data Q.    -   S102) Denoising and voxel grid filtering are performed on a        known point cloud data of docked wing to obtain a data set P1.        The data set P1 is reorganized through a data reorganization        method to obtain a key point set P. Meanwhile, a normal line of        each key point in the key point set P is calculated.    -   The step (S102) includes the following steps.        -   S102-1) The known point cloud data of the docked wing is            preprocessed to eliminate noise points that deviate from a            contour.        -   S102-2) The preprocessed point cloud data of the docked wing            is filtered through voxel grid filtering to obtain the data            set P1.        -   S103-3) A nearest neighbor of any key point in the data set            P1 is taken, and a search is performed through a k-nearest            neighbor algorithm—the data reorganization method to            reorganize the data set P1 according to a tree structure, so            as to obtain the key point set P.        -   S102-4) Dimensionality of each adjacent point of the key            point set P is reduced from a three-dimensional plane to a            two-dimensional plane through principal component analysis,            where the two-dimensional plane is a tangent plane of the            adjacent point, and a normal line of the tangent plane is            the normal line of the corresponding key point.    -   S103) A histogram feature descriptor in a normal direction of        any key point in the key point set P and a histogram feature        descriptor in a normal direction of any point in the skin point        cloud data Q are calculated, respectively;    -   The step (S103) includes the following steps.        -   S103-1) A local feature descriptor m_(i), i=1, 2, 3, . . . ,            k in a normal direction of any key point in the key point            set P is calculated, where k is the number of key points in            the key point set.        -   In this step, any key point in the key point set P is taken            as a center. A spherical area is constructed with a self-set            radius, and is divided into grids along three directions of            radial, azimuth, and elevation. The spherical area is            divided into 32 spatial areas through dividing along the            radial direction 2 times, the azimuth direction 8 times, and            the elevation direction 2 times.        -   In each spatial region, a cosine of an angle between a            normal line n_(N) of any point in the spatial region and a            normal line n_(i) of a key point p_(i) is calculated: cos            θ=n_(N)·n_(i), where N is the number of points in the            spatial region.        -   A histogram statistic is performed on the number of points            falling into each spatial region according to the cosine            value to obtain the local feature descriptor m_(i) of the            normal direction of the key point.        -   S103-2) calculating a local feature descriptor m_(j), j=1,            2, 3, . . . , l in a normal direction of any point in the            skin point cloud data Q using the same method, where l is            the number of key points in the skin point cloud data Q.    -   S104) A Euclidean distance between feature descriptors of two        points is calculated through a K-nearest neighbor algorithm.        Similar histogram feature descriptors are found. Points with        high similarity are added into a set M, and the set M is        initially set as an empty set.    -   The step (S104) includes the following steps.        -   S104-1) The feature descriptor m_(i) of the histogram of the            key point set P is input using KdTree, and a nearest            neighbor search is performed using fast library for            approximate nearest neighbors (FLANK).        -   S104-2) Among all the points in the skin point cloud data Q,            a point whose matching distance from any key point in the            key point set P is less than a Euclidean distance σ is            searched, that is, a feature point: σ=0.3.        -   S104-3) All the feature points whose matching distance is            less than a are put into the set M.    -   S105) A clustering is performed on the set M using a Hough        voting algorithm to obtain a local point cloud set P′ in the key        point set P that matches the skin point cloud data Q.    -   The step (S105) includes the following steps.        -   S105-1) A local reference frame for the feature points in            the skin point cloud data Q and the feature points in the            key point set P is calculated.        -   S105-2) The clustering is performed using the Hough voting            algorithm. For the input feature points of the skin point            cloud data Q and the input feature points of the key point            set P, a size of a Hough peak point in a Hough space is set            as a threshold.        -   S105-3) The set M is matched. According to the threshold set            in step (S105-2), a final cluster set, that is, the local            point cloud set P′, is identified.    -   S106) The skin point cloud data Q is matched with the local        point cloud set P′ through an iterative closest point algorithm.    -   The step (S106) includes the following steps.        -   S106-1) The skin point cloud data Q is matched with the            locked local point cloud set P′ using the iterative closest            point algorithm.        -   In this step, a corresponding near point, that is, a            corresponding point pair of any key point in the key point            set P in the skin point cloud data Q is calculated. A rigid            body transformation T that minimizes an average distance of            the corresponding point pair is obtained. A translation            parameter ω and a rotation parameter r are obtained. The key            point set P is transformed according to the translation            parameter ω and a rotation parameter r to obtain a new            transformed point set P″. If the new transformed point set            P″ and the skin point cloud data Q satisfy that an average            distance between the two point sets is less than a given            threshold, a result after coarse registration will be            obtained.        -   S106-2) Wrong points are filtered out in the coarse            registration using a global hypothesis verification            algorithm, so as to a skin registration.    -   S2) Fairing skin repair    -   As shown in FIG. 3, the step (S2) includes the following steps.        -   S201) A boundary line of a point cloud frame of the wing and            the skin point cloud data Q after coarse registration is            extracted using a random sample consensus (RANSAC)            extraction algorithm.        -   S202) The boundary line of the point frame is projected onto            the skin point cloud data Q. A distance between a projection            line on the point cloud and the boundary line is calculated,            so as to obtain an amount of skin to be repaired.        -   S203) The skin is cut according to the amount of skin to be            repaired, and then the repair is finished.

Those skilled in the art can understand that all or part of the steps inthe various methods of the above-mentioned embodiments can be completedby instructing a hardware through a program. The program can be storedin a computer-readable storage medium, and the storage medium is aread-only memory (ROM), a random-access memory (RAM), a disk or acompact disk (CD).

The above-mentioned embodiments are not intended to limit the scope ofthe present disclosure. For those skilled in the art, any replacementsand modifications without departing from the spirit of the presentdisclosure should fall in the scope of the appended claims.

What is claimed is:
 1. A fairing skin repair method based on measuredwing data, comprising: S1) carrying out a fairing skin registration,comprising: S101) obtaining skin point cloud data Q; S102) performingdenoising and voxel grid filtering on a known point cloud data of dockedwing to obtain a data set P1; reorganizing the data set P1 through adata reorganization method to obtain a key point set P; and calculatinga normal line of each key point in the key point set P; S103)calculating a histogram feature descriptor in a normal direction of anykey point in the key point set P and a histogram feature descriptor in anormal direction of any point in the skin point cloud data Q,respectively; S104) calculating a Euclidean distance between featuredescriptors of two points through a K-nearest neighbor algorithm;searching similar histogram feature descriptors; adding points with highsimilarity to a set M; and initially setting the set M as an empty set;S105) performing a clustering on the set M using a Hough votingalgorithm to obtain a local point cloud set P′ in the key point set Pthat matches the skin point cloud data Q; and S106) matching the skinpoint cloud data Q with the local point cloud set P′ through aniterative closest point algorithm; and S2) carrying out a fairing skinrepair.
 2. The method of claim 1, wherein in step (S101), a skinuniformly manufactured in a factory is scanned using a three-dimensionallaser scanner, so as to collect the skin point cloud data Q.
 3. Themethod of claim 1, wherein the step (S102) comprises: S102-1)preprocessing the known point cloud data of the docked wing to eliminatenoise points that deviate from a contour; S102-2) filtering thepreprocessed point cloud data of the docked wing through voxel gridfiltering to obtain the data set P1; S102-3) taking a nearest neighborof any key point in the data set P1, performing a search through ak-nearest neighbor algorithm—the data reorganization method toreorganize the data set P1 according to a tree structure, so as toobtain the key point set P; and S102-4) reducing dimensionality of eachadjacent point of the key point set P from a three-dimensional plane toa two-dimensional plane through principal component analysis; whereinthe two-dimensional plane is a tangent plane of the adjacent point, anda normal line of the tangent plane is the normal line of thecorresponding key point.
 4. The method of claim 3, wherein the step(S103) comprises: S103-1) calculating a local feature descriptor m_(i),i=1, 2, 3, . . . , k in a normal direction of any key point in the keypoint set P, wherein k is the number of key points in the key point set;wherein the step (S103-1) comprises: taking any key point in the keypoint set P as a center; constructing a spherical area with a self-setradius; dividing grids along three directions of radial, azimuth, andelevation; wherein the spherical area is divided into 32 spatial areasthrough dividing along the radial direction 2 times, the azimuthdirection 8 times, and the elevation direction 2 times; in each spatialregion, calculating a cosine of an angle between a normal line n_(N) ofany point in the spatial region and a normal line n_(i) of a key pointp_(i): cos θ=n_(N)·n_(i); wherein N is the number of points in thespatial region; and performing a histogram statistic on the number ofpoints falling into each spatial region according to the cosine value toobtain the local feature descriptor m_(i) of the normal direction of thekey point; S103-2) calculating a local feature descriptor m_(j), j=1, 2,3, . . . , l in a normal direction of any point in the skin point clouddata Q using a same method, wherein l is the number of key points in theskin point cloud data Q.
 5. The method of claim 4, wherein the step(S104) comprises: S104-1) inputting the local feature descriptor m_(i)of the histogram of the key point set P using KdTree; and performing anearest neighbor searching using fast library for approximate nearestneighbors (FLANK); S104-2) among all the points in the skin point clouddata Q, searching a point whose matching distance from any key point inthe key point set P is less than a Euclidean distance σ, that is, afeature point: σ=0.3; and S104-3) putting all the feature points whosematching distance is less than a into the set M.
 6. The method of claim5, wherein the step (S105) comprises: S105-1) calculating a localreference frame for the feature points in the skin point cloud data Qand the feature points in the key point set P; S105-2) performing aclustering using the Hough voting algorithm; for the input featurepoints of the skin point cloud data Q and the input feature points ofthe key point set P, setting a size of a Hough peak point in a Houghspace as a threshold; and S105-3) matching the set M, according to thethreshold set in step (S105-2), identifying a final cluster set, thatis, the local point cloud set P′.
 7. The method of claim 6, wherein thestep (S106) comprises: S106-1) matching the skin point cloud data Q withthe locked local point cloud set P′ using the iterative closest pointalgorithm; wherein the step (S106-1) comprises: calculating acorresponding near point, that is, a corresponding point pair of any keypoint in the key point set P in the skin point cloud data Q; obtaining arigid body transformation T that minimizes an average distance of thecorresponding point pair; obtaining a translation parameter ω and arotation parameter r; transforming the key point set P according to thetranslation parameter ω and a rotation parameter r to obtain a newtransformed point set P″; wherein if the new transformed point set P″and the skin point cloud data Q satisfy that an average distance betweenthe two point sets is less than a given threshold, a result after coarseregistration will be obtained; and S106-2) filtering out wrong points inthe coarse registration using a global hypothesis verificationalgorithm, so as to finish a skin registration.
 8. The method of claim7, wherein the step (S2) comprises: S201) extracting a boundary line ofa point cloud frame of the wing and the skin point cloud data Q aftercoarse registration using a random sample consensus (RANSAC) extractionalgorithm; S202) projecting the boundary line of the point frame ontothe skin point cloud data Q; calculating a distance between a projectionline on the point cloud and the boundary line, so as to obtain an amountof skin to be repaired; and S203) cutting the skin according to theamount of skin to be repaired; and finishing the repair.